Method of detecting lung cancer

ABSTRACT

A biomarker panel for a urine test for detecting lung cancer detects a biomarker selected from the group of biomarkers consisting of DMA, C5:1, C10:1, ADMA, C5-OH, SDMA, and kynurenine, or a combination thereof. A biomarker panel for a serum test for detecting lung cancer detects a biomarker selected from the group of biomarkers consisting of valine, arginine, ornithine, methionine, spermidine, spermine, diacetylspermine, C10:2, PC aa C32:2, PC ae C36:0, and PC ae C44:5; and lysoPC a C18:2, or a combination thereof.

RELATED APPLICATIONS

This application is the U.S. National Phase of and claims priority toInternational Patent Application No. PCT/CA2016/050758, InternationalFiling Date Jun. 27, 2016, entitled Method of Detecting Lung Cancer,which claims benefit of U.S. Provisional Application Ser. No. 62/185,213filed Jun. 26, 2015 entitled Method of Detecting Lung Cancer, both ofwhich are incorporated herein by reference in their entireties.

BACKGROUND OF THE INVENTION Field of the Invention

The present invention relates to a method of detecting cancer and, inparticular, to a method of detecting lung cancer by measuring polyaminemetabolites and other metabolites.

Description of the Related Art

The polyamine pathway has been demonstrated to be significantlyup-regulated in cancer cells. Spermidine/spermine N¹-acetyltransferase(SSAT) is recognized as a critical enzyme in the pathway and is highlyregulated in all mammalian cells. While SSAT is present in normaltissues in very low concentrations, it is present at much higher levelsin cancer cells. Therefore, as cellular levels of SSAT increase,measurement of its enzymatic activity correlates with the presence andseverity of cancer.

SUMMARY OF THE INVENTION

There is provided a method of detecting lung cancer by measuringpolyamine metabolites and other metabolites in urine and serum.

There is also provided a biomarker panel for a urine test for detectinglung cancer wherein the biomarker panel detects a biomarker selectedfrom the group of biomarkers consisting of DMA, C5:1, C10:1, ADMA,C5-OH, SDMA, and kynurenine, or a combination thereof. The biomarkerpanel may be used to diagnose lung cancer. The biomarker panel may beused to determine a stage of lung cancer. The biomarker panel may beused to screen for lung cancer. The biomarker panel may be used todetermine a treatment prognosis for lung cancer. The biomarker panel maybe used to determine efficacy of a drug during the development orclinical phase.

There is further provided a biomarker panel for a serum test fordetecting lung cancer wherein the biomarker panel detects a biomarkerselected from the group of biomarkers consisting of valine, arginine,ornithine, methionine, spermidine, spermine, diacetylspermine, C10:2, PCaa C32:2, PC ae C36:0, and PC ae C44:5; and lysoPC a C18:2, or acombination thereof. The biomarker panel may be used to diagnose lungcancer. The biomarker panel may be used to determine a stage of lungcancer. The biomarker panel may be used to screen for lung cancer. Thebiomarker panel may be used to determine a treatment prognosis for lungcancer. The biomarker panel may be used to determine efficacy of a drugduring the development or clinical phase.

There is still further provided a biomarker panel for a serum test fordetecting lung cancer wherein the biomarker panel detects a biomarkerselected from the group of biomarkers consisting of valine, C10:2, PC aaC32:2, PC ae C36:0, PC ae C44:5, or a combination thereof. The biomarkerpanel may be used to diagnose lung cancer. The biomarker panel may beused to determine a stage of lung cancer. The biomarker panel may beused to screen for lung cancer. The biomarker panel may be used todetermine a treatment prognosis for lung cancer. The biomarker panel maybe used to determine efficacy of a drug during the development orclinical phase.

There is yet still further provided a panel for a serum test fordetecting late stage lung cancer wherein the biomarker panel detects abiomarker selected from the group of biomarkers consisting of valine,diacetylspermine, spermine, C10:2, and lysoPC a C18.2, or a combinationthereof. The biomarker panel may be used to diagnose lung cancer. Thebiomarker panel may be used to determine a stage of lung cancer. Thebiomarker panel may be used to screen for lung cancer. The biomarkerpanel may be used to determine a treatment prognosis for lung cancer.The biomarker panel may be used to determine efficacy of a drug duringthe development or clinical phase.

BRIEF DESCRIPTIONS OF DRAWINGS

The invention will be more readily understood from the followingdescription of the embodiments thereof given, by way of example only,with reference to the accompanying drawings, in which:

FIG. 1 is a box-and-whiskers plot showing the concentrates ofmetabolites in healthy patients and cancer patients;

FIG. 2 is a partial least squares discriminant analysis (PLS-DA) plotshowing separation between control patients and lung cancer patientsbased on an analysis of urine samples;

FIG. 3 is a variable importance in projection (VIP) analysis plotranking discriminating urine metabolites in descending order ofimportance;

FIG. 4 is a receiver operating characteristic (ROC) analysis includingthe seven most important metabolites from VIP analysis of urine samplesshown in FIG. 3 ;

FIG. 5 is a partial least squares discriminant analysis (PLS-DA) plotshowing separation between control patients and lung cancer patientsbased on an analysis of serum samples;

FIG. 6 is a variable importance in projection (VIP) analysis plotranking discriminating serum metabolites in descending order ofimportance;

FIG. 7 is a receiver operating characteristic (ROC) including the fivemost important metabolites from VIP analysis of serum samples shown inFIG. 6 ;

FIG. 8 is a table showing a univariate analysis of individualmetabolites in serum samples at time T1;

FIG. 9 is a table showing a univariate analysis of individualmetabolites in serum samples at time T2;

FIG. 10 is a principle component analysis (PCA) plot showing separationbetween control patients and lung cancer patients based on an analysisof serum samples at time T1;

FIG. 11 is a three-dimensional principle component analysis (PCA) plotshowing separation between control patients and lung cancer patientsbased on an analysis of serum samples at time T1;

FIG. 12 is a partial least squares discriminant analysis (PLS-DA) plotshowing separation between control patients and lung cancer patientsbased on an analysis of serum samples at time T1;

FIG. 13 is a three-dimensional partial least squares discriminantanalysis (PLS-DA) plot showing separation between control patients andlung cancer patients based on an analysis of serum samples at time T1;

FIG. 14 is a variable importance in projection (VIP) analysis plotranking discriminating serum metabolites in descending order ofimportance at time T1;

FIG. 15 is a receiver operating characteristic (ROC) analysis includingthe five most important metabolites from the VIP analysis of serumsamples shown in FIG. 14 ;

FIG. 16 is a principle component analysis (PCA) plot showing separationbetween control patients and lung cancer patients based on an analysisof serum samples at time T2;

FIG. 17 is a three-dimensional principle component analysis (PCA) plotshowing separation between control patients and lung cancer patientsbased on an analysis of serum samples at time T2;

FIG. 18 is a partial least squares discriminant analysis (PLS-DA) plotshowing separation between control patients and lung cancer patientsbased on an analysis of serum samples at time T2;

FIG. 19 is a three-dimensional partial least squares discriminantanalysis (PLS-DA) plot showing separation between control patients andlung cancer patients based on an analysis of serum samples at time T2;

FIG. 20 is a variable importance in projection (VIP) analysis plotranking discriminating serum metabolites in descending order ofimportance at time T2; and

FIG. 21 is a receiver operating characteristic (ROC) analysis includingthe five most important metabolites from the VIP analysis of serumsamples shown in FIG. 20 .

DESCRIPTIONS OF THE PREFERRED EMBODIMENTS

Serum ampler from control patients, early stage cancer patients, andlate stage cancer patients were analyzed using a combination of directinjection mass spectrometry and reverse-phase LC-MS/MS. An AbsoluteIDQ®p180 Kit obtained from Biocrates Life Sciences AG of Eduard-Bodem-Gasse8 6020, Innsbruck, Austria was used in combination with an API4000Qtrap® tandem mass spectrometer obtained from Applied Biosystems/MDSSciex of 850 Lincoln Centre Drive, Foster City, Calif., 94404, UnitedStates of America, for the targeted identification and quantification ofup to 180 different endogenous metabolites including amino acids,acylcarnitines, biogenic amines, glycerophospholipids, sphingolipids andsugars.

The method used combines the derivatization and extraction of analytes,and the selective mass-spectrometric detection using multiple reactionmonitoring (MRM) pairs. Isotope-labeled internal standards and otherinternal standards are integrated in AbsoluteIDQ® p180 Kit plate filterfor metabolite quantification. The AbsoluteIDQ® p180 Kit contains a 96deep-well plate with a filter plate attached with sealing tape as wellas reagents and solvents used to prepare the plate assay. First 14 wellsin the AbsoluteIDQ® p180 Kit were used for one blank, three zerosamples, seven standards and three quality control samples provided witheach AbsoluteIDQ® p180 Kit. All the serum samples were analyzed with theAbsoluteIDQ® p180 Kit using the protocol described in the AbsoluteIDQ®p180 Kit User Manual.

Serum samples were thawed on ice and were vortexed and centrifuged at2750×g for five minutes at 4° C. 10 μL of each serum sample was loadedonto the center of the filter on the upper 96-well kit plate and driedin a stream of nitrogen. 20 μL of a 5% solution of phenyl-isothiocyanatewas subsequently added for derivatization. The filter spots were thendried again using an evaporator. Extraction of the metabolites was thenachieved by adding 300 μL methanol containing 5 mM ammonium acetate. Theextracts were obtained by centrifugation into the lower 96-deep wellplate. This was followed by a dilution step with MS running solvent fromthe AbsoluteIDQ® p180 Kit.

Mass spectrometric analysis was performed on the API4000 Qtrap® tandemmass spectrometer which was equipped with a solvent delivery system. Theserum samples were delivered to the mass spectrometer by either a directinjection (DI) method or liquid chromatography method. The BiocratesMetIQ™ software, which is integral to the AbsoluteIDQ® p180 Kit, wasused to control the entire assay workflow, from sample registration toautomated calculation of metabolite concentrations to the export of datainto other data analysis programs. A targeted profiling scheme was usedto quantitatively screen for known small molecule metabolites usingmultiple reaction monitoring, neutral loss, and precursor ion scans.

First Study

Metabolites were detected and quantified in urine samples collected from10 control patients and 12 lung cancer patients undergoing chemotherapytreatment using LC-MS/MS-based assay. In particular, the followingpolyamine pathway metabolites: spermidine, spermine, methionine,putrescine, methylthioadenosine (MTA), S-adenosyl-L-methionine (SAMe),ornithine, arginine, N-acetylspermine, and N-acetylspermidine weredetected and quantified in urine samples.

The results of this study, shown in FIG. 1 , indicate that fourmetabolites have been identified as putative biomarkers for cancer,namely, spermidine, ornithine, arginine and methionine. The results fromthis study revealed a preliminary picture of the polyamine metabolome incancer patients and healthy subjects.

Second Study

Metabolites were detected in urine and serum samples collected from 15control patients and 31 lung cancer patients (including 7 early stagecancer patients). The samples were analyzed using a combined directinjection mass spectrometry (MS) and reverse-phase LC-MS/MS as describedabove. Statistical analysis was performed using MetaboAnalyst andROCCET.

The following metabolites were identified and quantified using theBiocrates Absolute p180IDQ™ Kit:

Metabolite Serum Urine Amino Acids 21 21 Acylcarnitines 23 35 Biogenicamines 13 17 Glycerophospholipids 85 32 (PCs & LysoPCs) Sphingolipids 156 Hexose 1 1

PLS Discriminant Analysis (PLS-DA) resulted in detectable separation oflung cancer patients and control patients based on seven metabolites inurine, as shown in FIG. 2 , and five metabolites in serum, as shown inFIG. 5 .

Total dimethylarginine in asymmetric and symmetric forms (DMA),tiglylcarnitine (C5:1), decenoylcarnitine (C10:1), asymmetricdimethylarginine (ADMA), hydroxyvalerylcarnitine (C5-OH), symmetricdimethylarginine (SDMA), and kynurenine appear to be the seven mostimportant urinary metabolites for distinguishing lung cancer based onvariable importance in projection (VIP) analysis as shown in FIG. 3 . Areceiver operating characteristic (ROC) analysis including the sevenmost important metabolites from VIP analysis of urine samples is shownin FIG. 4 .

Valine, decadienylcarnitine (C10:2), glycerophosopholipids (PC aa C32:2;PC ae C36:0, and PC ae C44:5) appear to be the five most important serummetabolites for distinguishing lung cancer based on variable importancein projection (VIP) analysis as shown in FIG. 6 . A receiver operatingcharacteristic (ROC) analysis including the five most importantmetabolites from VIP analysis of serum samples is shown in FIG. 7 .

Seven putative urinary biomarkers and five putative serum biomarkershave accordingly been identified for diagnosis of lung cancer and may beused in a biomarker panel for a urine test or serum test to detect lungcancer.

Third Study

Metabolites were detected in serum samples collected from 26 late stagelung cancer patients and 15 control patients at times T1 and T2. Inparticular, the following polyamine pathway metabolites: valine,arginine, ornithine, methionine, spermidine, spermine, diacetylspermine,decadienylcarnitine (C10:2), glycerophosopholipids (PC aa C32:2 and PCae C36:0), lysoPC a C18:2, methylthioadenosine, and putrescine weredetected and quantified in the serum samples at times T1 and T2.

The samples were analyzed using a combined direct injection massspectrometry (MS) and reverse-phase LC-MS/MS as described above.Statistical analysis was performed using MetaboAnalyst and ROCCET.Methylthioadenosine and putrescine were however excluded from theanalysis because the missing rates were greater than 50%. FIGS. 8 and 9respectively show the results of a univariate analysis of the remainingindividual metabolites at times (T1) and (T2).

Principal component analysis (PCA) and partial least squaresdiscriminant analysis (PLS-DA) at time T1 resulted in a detectableseparation of lung cancer patients and control patients based on elevenmetabolites in serum as shown in FIGS. 10 to 13 .

Total valine, diacetylspermine, spermine, lysoPC a C18.2, anddecadienylcarnitine (C10:2) appear to be the five most important serummetabolites for distinguishing late stage lung cancer based on variableimportance in projection (VIP) analysis as shown in FIG. 14 . A receiveroperating characteristic (ROC) analysis including the five mostimportant metabolites from VIP analysis of serum samples is shown inFIG. 15 .

Principal component analysis (PCA) and partial least squaresdiscriminant analysis (PLS-DA) at time T2 resulted in a detectableseparation of lung cancer patients and control patients based on elevenmetabolites in serum as shown in FIGS. 16 to 19 .

Total valine, diacetylspermine, spermine, lysoPC a C18.2, anddecadienylcarnitine (C10:2) again appear to be the five most importantserum metabolites for distinguishing late stage lung cancer based onvariable importance in projection (VIP) analysis as shown in FIG. 20 . Areceiver operating characteristic (ROC) analysis including the five mostimportant metabolites from VIP analysis of serum samples is shown inFIG. 21 .

Eleven putative serum biomarkers have accordingly been identified fordiagnosis of late stage lung cancer and may be used in a biomarker panelfor a serum test to detect lung cancer.

It will be understood by a person skilled in the art that many of thedetails provided above are by way of example only, and are not intendedto limit the scope of the invention which is to be determined withreference to the following claims.

What is claimed is:
 1. A method for processing a human clinical bloodsample, the method comprising obtaining a blood sample from a subjectclinically assessed as having or suspected of having lung cancer, andquantifying a panel of metabolites in said blood sample, the panelcomprising at least one blood metabolite selected from diacetylspermine,C10:2, PC aa C32:2, PC ae C36:0, and PC ae C44:5, wherein the bloodsample is or comprises serum.
 2. The method of claim 1, wherein thepanel comprises at least two of said blood metabolites.
 3. The method ofclaim 1, wherein the panel comprises at least three of said bloodmetabolites.
 4. The method of claim 1, wherein the at least one bloodmetabolite comprises diacetylspermine.
 5. The method of claim 1, whereinthe at least one blood metabolite comprises C10:2.
 6. The method ofclaim 1, wherein the at least one blood metabolite comprises PC aaC32:2.
 7. The method of claim 1, wherein the at least one bloodmetabolite comprises PC ae C36:0.
 8. The method of claim 1, wherein theat least one blood metabolite comprises PC ae C44:5.